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on Big Data |
| By: | Yue-Gang Song; Yu-Long Zhou; Ren-Jie Han |
| Abstract: | Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.11317&r=big |
| By: | Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb |
| Abstract: | Based on recent developments in the field of artificial intelligence (AI), we examine what type of human labor will be a substitute versus a complement to emerging technologies. We argue that these recent developments reduce the costs of providing a particular set of tasks – prediction tasks. Prediction about uncertain states of the world is an input into decision-making. We show that prediction allows riskier decisions to be taken and this is its impact on observed productivity although it could also increase the variance of outcomes as well. We consider the role of human judgment in decision-making as prediction technology improves. Judgment is exercised when the objective function for a particular set of decisions cannot be described (i.e., coded). However, we demonstrate that better prediction impacts the returns to different types of judgment in opposite ways. Hence, not all human judgment will be a complement to AI. Finally, we show that humans will delegate some decisions to machines even when the decision would be superior with human input. |
| JEL: | D81 O3 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24626&r=big |
| By: | Michael C. Knaus |
| Abstract: | This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high-dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1805.10300&r=big |
| By: | Knaus, Michael C. (University of St. Gallen) |
| Abstract: | This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high-dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice. |
| Keywords: | double machine learning, extracurricular activities, music, cognitive and non-cognitive skills, youth development |
| JEL: | J24 Z11 C21 C31 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp11547&r=big |
| By: | Liberali, G. |
| Keywords: | e-commerce, informatiemaatschappij, adverteren, kunstmatige intelligentie, machine learning, multi-armed bandits, marketing science, online advertising, digital marketing, clinical trials |
| JEL: | C44 M31 |
| Date: | 2018–05–25 |
| URL: | http://d.repec.org/n?u=RePEc:ems:euriar:107428&r=big |
| By: | Prüfer, Jens (Tilburg University, TILEC); Prüfer, Patricia (Tilburg University, TILEC) |
| Abstract: | To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory. |
| Keywords: | data science; maching learning; institutions; text analysis |
| JEL: | C50 C53 C87 D02 K0 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutil:4392ac65-4fb6-4e9a-a92d-5da46339c7a9&r=big |
| By: | Lu, Richard (University of California, Berkeley); Chatman, Jennifer A. (University of California, Berkeley); Goldberg, Amir (Stanford University); Srivastava, Sameer B. (University of California, Berkeley) |
| Abstract: | From the schoolyard to the boardroom, the pressures of cultural assimilation pervade all walks of social life. Yet people vary in the capacity to fit in culturally, and their fit can wax and wane over time. We examine how individual cognition and social influence produce variation and change in cultural fit. We do so by lifting the curtain between the backstage (cognition) and frontstage (behavior) of cultural fit. We theorize that the backstage comprises two analytically distinct dimensions--perceptual accuracy and value congruence--and that the former matters for normative compliance on the frontstage, whereas the latter does not. We further propose that a person's behavior and perceptual accuracy are both influenced by observations of others' behavior, whereas value congruence is less susceptible to peer influence. Drawing on email and survey data from a mid-sized technology firm, we use the tools of computational linguistics and machine learning to develop longitudinal measures of frontstage and backstage cultural fit. We also take advantage of a reorganization that produced quasi-exogenous shifts in employees' peer groups to identify the causal impact of social influence. |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:repec:ecl:stabus:3603&r=big |